Relapsed Leukemia Prognosis via NT5C2 Mutation Detection
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Solution Overview
Problem
Current methods for treating relapsed acute lymphoblastic leukemia (ALL) are ineffective due to intrinsic drug resistance, and the biological pathways mediating this resistance are not well understood, leading to a poor prognosis for patients despite intensive retreatment.
Innovation Solution
The method involves detecting mutations in specific genes such as NT5C2, RGS12, and others using RNA sequencing, which predict a higher likelihood of relapse, allowing for targeted therapy and prevention of relapse by inhibiting NT5C2 gene expression or activity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If intensive retreatment strategies (allogeneic stem cell transplant or prolonged chemotherapy) are applied to relapsed ALL patients, then survival outcomes are attempted to be improved, but the intrinsic drug resistance of relapsed clones renders these treatments ineffective
Solution Approach 1:
The patent performs whole transcriptome sequencing at diagnosis to identify relapsed ALL clones and their drug resistance mechanisms before retreatment begins. This preliminary molecular characterization allows selection of patients who will respond to intensive retreatment versus those with intrinsic resistance, enabling preliminary stratification that prevents futile aggressive treatments.
Solution Approach 2:
The patent changes the approach from empirical treatment selection to molecularly-guided therapy by analyzing transcriptome parameters (gene expression profiles) to identify specific molecular signatures of drug resistance. This parameter-based classification transforms treatment decisions from population-based protocols to individualized molecular profiles.
2Loss of information
If whole transcriptome sequencing is performed to analyze relapsed ALL clones, then comprehensive understanding of drug resistance mechanisms is achieved, but the complexity and cost of the analysis increases
Solution Approach 1:
The patent extracts only the most relevant biological pathways and gene expression signatures from the whole transcriptome data that are specifically associated with drug resistance in relapsed ALL. Rather than analyzing all genes equally, the method focuses on extracting key molecular features (such as pathways involved in apoptosis resistance, DNA repair, or drug metabolism) that predict treatment response.
Solution Approach 2:
The patent performs preliminary bioinformatic processing and filtering of transcriptome data to identify pre-defined gene sets and pathways relevant to ALL pathogenesis and drug resistance. This preliminary analysis framework reduces the complexity of interpreting thousands of genes by focusing on curated biological pathways known to be involved in leukemia progression and treatment response.
Data Source
AI summary
The present invention is directed to methods of prognosing relapsed leukemia in a subject. These methods are based on the detection of one or more relapse-specific gene mutations in a patient sample. The present invention further relates to methods of preventing and treating relapse leukemia in a subject based on the determined prognosis of the subject.